DATE:

Friday, April 17, 2020

TIME:

3:00 PM

LOCATION:

Virtual Zoom Conference

SPEAKER:

Dr. Matthias Morzfeld, Associate Professor, Institute of Geophysics and Planetary Physics, University of CA, San Diego

ABSTRACT:

I will first review Bayesian inference, which means to incorporate information from observations (data) into a numerical model, and will give some examples of applications in Earth science. The numerical solution of Bayesian inference problems is often based on sampling a posterior probability distribution. Sampling posterior distributions is difficult because these are usually high-dimensional (many parameters or states to estimate) and non-standard (e.g., not Gaussian). In particular a high-dimension causes numerical difficulties and slow convergence in many sampling algorithms. I will explain how ideas from numerical weather prediction can be leveraged to design Markov chain Monte Carlo (MCMC) samplers whose convergence rates are independent of the problem dimension for a well-defined class of problems.

HOST:

Samuel Shen

VIDEO: